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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Isozaki, Atsushi Fukae, Kazuki Arai, Kenichi Kobayashi, Toru Nakamura, Masahide Nagasaki University Computer and Information Science Program Graduate School of Engineering Nagasaki Japan Nagasaki University Division of Electrical Engineering and Computer Science Graduate School of Engineering Nagasaki Japan Kobe University Center of Mathematical and Data Sciences Hyogo Japan

ISBN: (纸本)9781665492324

This study is related to a system that enables elderly people to communicate interactively with young people who use existing message exchange services by simply speaking to an avatar on a tablet PC, without having to perform complicated operations of the tablet PC. On the other hand, the young people side can bring in elderly people who could not previously participate in existing message exchange services groups. The effectiveness of the system was verified through demonstration experiments with two households consisting of elderly people in their 80s. © 2022 IEEE.

关键词： Personal computers

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A Research on Scattering Removal Technology Using Compact GPU Machine for Real-Time Visualization 23

A Research on Scattering Removal Technology Using Compact GP...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Ono, Seiya Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a system that enables visualization under the situation of scattering media such as fog and smoke by Peplography which is scattering media removal system, on a small GPU machine. Compared to conventional haze removal techniques, peplography technology is capable of removing more dense scattering media and can handle cases where the density of fog varies in the image. On the other hand, the conventional peplography process is performed on a desktop PC, and no system has yet been developed to perform peplography in an outside environment. Thus, we have developed a peplography system using the Jetson AGX Xavier, a single-board computer equipped with a GPU, which is superior to conventional methods in terms of portability, execution speed, and power consumption. We also demonstrate the performance of this system by conducting experiments using this system under a simulated scattering media scene and evaluating the obtained results in terms of FPS and the image evaluation metric. © 2023 ICROS.

关键词： Graphics processing unit

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Analysis of Accurate Focal Points and Automatic Method of Digital Holographic Microscopy (DHM) for Obtaining an Accurate 3D Profile 23

Analysis of Accurate Focal Points and Automatic Method of Di...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Ono, Taishi Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

Currently, Digital Holographic Microscopy(DHM) is used for researching disease diagnosis or microbes. It cannot obtain the correct three-dimensional (3D) profile for a little noise because biological cells are microscopic. Then we need images of the focal point of DHM for reducing noise. However, in DHM, the depth of field becomes smaller influenced by the objective lens. In addition, focusing to obtain hologram images of biological cells in conventional DHM systems is entirely manual, because the decision of the focal point is made by the photographer's senses, which makes it even more difficult to focus on the object. These factors make it difficult for the conventional DHM system to acquire images of the focal points. Therefore, in this paper, we propose the analysis of accurate focusing points using a noise map. This eliminates the illusion or misperception that is a drawback of the manual. We improved an accurate 3D profile. 3D profiles were created at the focal point by the proposed method and at the positions before and after the focal points, and evaluated for SNR. From this, the proposed method can be used to analyze biological cells and microstructures. © 2023 ICROS.

关键词： Holograms

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Sudden Cardiac Arrest Detection Based on Temporal Features of ECG Using Support Vector Machine Classifier

Sudden Cardiac Arrest Detection Based on Temporal Features o...

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International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Banerjee, Prakash Bhattacherjee, Saptak Dasgupta, Kousik Department of Electrical and Electronics Engineering University of Engineering and Management Kolkata India School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Howrah Shibpur India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani India

ISBN: (纸本)9789811670107

Sudden cardiac arrest (SCA) is caused due to malfunctioning in heart rhythm, and the patient needs to be hospitalized as early as possible just after the onset of SCA;otherwise, it can lead to the death of the patient. Therefore, detection of SCA with a very high accuracy and within a very short time after the onset of symptoms is very crucial for patient survival. Several approaches for detection of SCA have been made using temporal and morphological variations in the electrocardiogram (ECG). However, the existing methods suffer from low accuracy or involve long-term monitoring of ECG. Therefore, in the present work, an intelligent algorithm to detect SCA based on the extraction of temporal features like RR interval and the QT interval from ECG and subsequently classifying them using support vector machine (SVM) has been proposed. Experimental evaluation of the proposed method on five SCA and five normal subjects demonstrates a high classification accuracy of 100% which proves the superiority of the proposed algorithm as an effective method for detection of SCA. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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A Scalable Real-time Semantic Segmentation Network for Autonomous Driving 1

A Scalable Real-time Semantic Segmentation Network for Auton...

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1st Workshop on Advanced Multimedia Computing for Smart Manufacturing and engineering, AMC-SME 2023

作者： Liu, Jun Wu, Chao Yuan, Geng Niu, Wei Zhang, Wenbin Song, Houbing Herbert Robotics Institute of School of Computer Science Carnegie Mellon University Pittsburgh United States Department of Electrical & Computer Engineering Northeastern University Boston United States School of Computing University of Georgia Athens United States Department of Computer Science Michigan Technological University Houghton United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

ISBN: (纸本)9798400702730

Computational resources are limited on real-time embedded devices, so the available computing cost of deployment on the target platform must be considered. We develop a feature extraction module based on the MobileNet backbone that can be adjusted in terms of computational complexity and capacity using the depth multiplier parameter, classifier depth, and kernel depth. These three parameters allow us to control the count of channels within the network, effectively managing the model's capacity and computational requirements. To achieve semantic segmentation, we incorporate additional components, such as an extension module. This extension module typically includes 1x1 pointwise convolutional layers for pixel-level classification and a transposed convolutional layer for upsampling the output to the original input image size. By combining the feature extraction module with this extension module, we can create a complete architecture capable of performing semantic segmentation tasks. The feature extraction module provides the initial feature extraction and the extension module adds the necessary components for accurate pixel-wise classification and upsampling. Compared to Hardware-aware Neural Architecture Search (NAS), pruning, runtime pruning, and knowledge distillation methods, our model has several advantages in terms of modular design, structural controllability, ease of implementation, and cost-effectiveness. Our computational efficiency, as measured by FLOPS, is highly competitive. Our method is distinguished by solving the problem of MobileNet's inability to adjust the size and number of convolution kernels. It achieves this through adaptable parameter tuning, including MobileNet's depth multiplier, the kernel size in the FCN head's Separable Convolution layer, and the depth of the first Point-wise Convolution layer. These adjustments are customized to match hardware's max multiply-accumulates (MACs), optimizing network capacity and maximizing resource utilizat

关键词： Feature extraction

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Improvement of Depth Mapping and Occlusion Removal Method Using Integral Imaging 23

Improvement of Depth Mapping and Occlusion Removal Method Us...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Sakanoue, Haruka Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose the depth mapping and occlusion removal method using integral imaging (InIm). InIm is a method to obtain 3D information by capturing and reconstructing an object from multiple viewpoints. InIm can remove occlusion using parallax obtained by photographing an object from multiple viewpoints. However, occlusion removal uses a unit matrix to calculate the number of overlaps, which results in an overall darkening of the image. To solve this problem, this paper aims to improve occlusion removal by using the matrix of the mask image to calculate the number of overlaps. InIm can provide depth mapping that shows the distance from an object as pixel values. However, when there is an occlusion in front of the object, it is difficult to provide depth mapping object due to the influence of the occlusion. To solve this problem, we propose a method of depth mapping and occlusion removal method using InIm. Our method is expected to make great progress in improving safety in the field of autonomous driving. © 2023 ICROS.

关键词： Mapping

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Photon-Counting Integral Imaging Using Bayesian Estimation with Depth Camera Information 23

Photon-Counting Integral Imaging Using Bayesian Estimation w...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Uchino, Hideaki Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a method for visualizing object information under low light conditions using photon-counting integral imaging and depth images as prior information. To visualize 3D objects under low-light conditions, computational photon-counting imaging may be used. It is possible to estimate photons using statistical methods such as the Poisson distribution. Therefore, photon-counting integral imaging can visualize 3D images under low-light conditions by maximum likelihood estimation (MLE). However, MLE does not consider prior information so it may not be an accurate estimation. In addition, it is difficult to obtain accurate depth information as the distance increases due to rounding errors caused by computational reconstruction. Therefore, we propose Bayesian estimation such as a maximum a posteriori estimation method that uses as a priori information images from a depth camera, which can be acquired even in low-light conditions, to improve the quality of 3D images. To evaluate image quality of the proposed method, optical experiments were conducted by calculating image quality metrics. This technology can be utilized for night vision applications, such as object recognition in security camera systems and autonomous driving, using RGB cameras and depth cameras. © 2023 ICROS.

关键词： Maximum likelihood estimation

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Feature Extraction of Motor Imagination EEG Signals for a Collaborative Exoskeleton Robot Based on PSD Analysis 42

Feature Extraction of Motor Imagination EEG Signals for a Co...

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42nd Chinese Control Conference, CCC 2023

作者： Wu, Yunfeng Li, Junze Ren, Zichen Lu, Bo Zhang, Changzhu Qi, Peng Tongji University Department of Control Science and Engineering No. 1239 Siping Road Shanghai200092 China Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States School of Mechanicaland Electric Engineering Soochow University Robotics and Microsystems Center Suzhou215021 China

ISBN: (纸本)9789887581543

The integration of brain-machine interface and exoskeleton robot has been widespread application in gait correction, walking assistance, and numerous other scenarios. To effectively extract the electroencephalogram (EEG) signal features of motor imagery while wearing an exoskeleton, this study proposes a frequency band pre-determination method based on power spectral density (PSD) that enables common spatial patterns (CSP) to extract features from the frequency band with the highest energy in the EEG. The signal power spectral density of all channels is obtained at or near the average frequency of the maximum short interval frequency component energy. A second round of filtering is performed on the data, and the signal components are used as input for the subsequent feature extraction step. The CSP method extracts features from the spatial domain signal and generates feature maps. Finally, Support Vector Machines (SVM) are utilized to classify the EEG signals. Based on the pre-set gait of the exoskeleton robot and the motor imagery paradigm, feature extraction and classification of motor imagery EEG data during exoskeleton use were conducted, with an average accuracy rate of 77%. The experimental results demonstrate the effectiveness of this method in extracting the motor imagery EEG features during exoskeleton use. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Electroencephalography

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A Novel Approach to Continual Knowledge Transfer in Multilingual Neural Machine Translation Using Autoregressive and Non-Autoregressive Models for Indic Languages

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IEEE Access 2025年 13卷 89775-89810页

作者： Sheshadri, Shailashree K. Gupta, Deepa Paul, Biswajit Siva Bhavani, J. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India DRDO Centre for Artificial Intelligence and Robotics CV Raman Nagar Bangalore India

Recent progress in multilingual pre-trained models has significantly improved translation quality for Indic languages. However, extending these models to new languages via fine-tuning or retraining remains computationally costly and often leads to parameter interference, degrading performance on previously learned or typologically distant languages. While continual learning offers a promising alternative for incremental language addition, its application in Indic contexts is still limited and faces challenges in generalization across diverse linguistic settings. To overcome these issues, we propose a Continual Knowledge Transfer (CKT) framework for efficient and scalable multilingual adaptation. CKT is realized in both autoregressive (MNMT) and non-autoregressive (Switch-GLAT) architectures, yielding two variants: MNMT+CKT and Switch-GLAT+CKT. Rather than retraining the entire model, CKT freezes the multilingual base and updates only parameters relevant to the newly added *** innovations include gradient-based knowledge pruning, sequential teacher integration, and dynamic vocabulary expansion for minimizing interference and maximizing cross-lingual retention. Comprehensive evaluations on the IN22-Conv and IN22-Gen benchmark datasets demonstrate that both MNMT+CKT and Switch-GLAT+CKT consistently outperform established baselines, such as IndicTrans2, Google Translate, GPT-4-32K, LLaMA-2-17B, and NLIP-LAB-IITH. The proposed multi-step distillation approach, MNMT+CKT, consistently outperforms conventional fine-tuning and Knowledge Transfer (MNMT+KT) strategies for incremental adaptation of linguistically diverse Indic languages. On IN22-Conv, BLEU improvements range from +4.93 (Kashmiri → English) to +11.48 (Assamese → English) and similar improvements are seen for IN22-Gen. The method also achieves substantial reductions in trainable parameters—19.80% (Nepali) to 66.87% (Kannada)—while enabling up to 4x faster inference when integrated with the Switch-GLAT ar

关键词： Neural machine translation

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